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Retinal Ganglion Cells

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The quantization error in a Self-Organizing Map as a contrast and colour specific indicator of single-pixel change in large random patterns.

Neural networks : the official journal of the International Neural Network Society Oct 11, 2019
The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical time series a...
Deep Learning Models, Neurological Image Interpretation, Computer-Assisted Retinal Ganglion Cells Contrast Sensitivity Color Vision Animals
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Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps.

Ophthalmology Sep 30, 2019
PURPOSE: To develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images.
Humans Optic Disk Retinal Ganglion Cells Visual Fields Visual Field Tests Optic Nerve Diseases Diagnostic Techniques, Ophthalmological Middle Aged Deep Learning Vision Disorders Predictive Value of Tests Nerve Fibers Glaucoma Female Aged Adult Male
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Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs.

Ophthalmology Sep 24, 2019
PURPOSE: To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous optic neuropathy (GON) and optic nerve head (ONH) features from color fundus images, to determine the relative importance of these features in referr...
Referral and Consultation Datasets as Topic Nerve Fibers Optic Disk Optic Nerve Diseases Humans Retinal Ganglion Cells Ophthalmologists Retrospective Studies Male Glaucoma, Open-Angle Female Middle Aged Specialization Aged Deep Learning Sensitivity and Specificity ROC Curve Area Under Curve
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Machine Learning Models for Diagnosing Glaucoma from Retinal Nerve Fiber Layer Thickness Maps.

Ophthalmology. Glaucoma Aug 23, 2019
PURPOSE: To assess the diagnostic accuracy of multiple machine learning models using full retinal nerve fiber layer (RNFL) thickness maps in detecting glaucoma.
Humans Glaucoma Machine Learning Tomography, Optical Coherence Nerve Fibers Optic Disk Retinal Ganglion Cells Visual Fields Female Aged Intraocular Pressure Male Case-Control Studies
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Accuracy of machine learning for differentiation between optic neuropathies and pseudopapilledema.

BMC ophthalmology Aug 9, 2019
BACKGROUND: This study is to evaluate the accuracy of machine learning for differentiation between optic neuropathies, pseudopapilledema (PPE) and normals.
Eye Diseases, Hereditary Optic Neuritis Tomography, Optical Coherence Nerve Fibers Optic Disk Retinal Ganglion Cells Optic Nerve Diseases Humans Machine Learning Reproducibility of Results ROC Curve Visual Acuity Diagnosis, Differential
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A feature agnostic approach for glaucoma detection in OCT volumes.

PloS one Jul 1, 2019
Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly employed for the diagnosis and monitoring of glaucoma...
Aged Aged, 80 and over Young Adult Adult Male Middle Aged Deep Learning Humans Logistic Models Tomography, Optical Coherence Neural Networks, Computer Optic Disk Glaucoma Retinal Ganglion Cells Machine Learning Algorithms Female
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Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques.

PloS one May 6, 2019
OBJECTIVE: To compare axonal loss in ganglion cells detected with swept-source optical coherence tomography (SS-OCT) in eyes of patients with multiple sclerosis (MS) versus healthy controls using different machine learning techniques. To analyze the ...
Retinal Ganglion Cells Macula Lutea Male Multiple Sclerosis, Relapsing-Remitting Middle Aged Image Processing, Computer-Assisted Tomography, Optical Coherence Early Diagnosis Humans Machine Learning Female Adult Cross-Sectional Studies
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A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs.

American journal of ophthalmology Jan 26, 2019
PURPOSE: To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch membrane opening (BMO-MRW) from spectral-domain optical coherence tomography (SDOCT)...
Visual Field Tests Optic Nerve Diseases Photography Glaucoma, Open-Angle Bruch Membrane Tomography, Optical Coherence Humans Nerve Fibers Male Algorithms Optic Disk Middle Aged Retrospective Studies Retinal Ganglion Cells Deep Learning Female Visual Fields Vision Disorders Aged Area Under Curve Aged, 80 and over Cross-Sectional Studies Intraocular Pressure
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From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs.

Ophthalmology Dec 20, 2018
PURPOSE: Previous approaches using deep learning (DL) algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative s...
Photography Aged, 80 and over Glaucoma, Open-Angle Cross-Sectional Studies Humans Nerve Fibers Intraocular Pressure Deep Learning Tonometry, Ocular Male Algorithms ROC Curve Gonioscopy Optic Disk Middle Aged Female Visual Acuity Retinal Ganglion Cells Tomography, Optical Coherence Aged Visual Fields Fundus Oculi Visual Field Tests Optic Nerve Diseases
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Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images.

American journal of ophthalmology Oct 12, 2018
PURPOSE: We sought to construct and evaluate a deep learning (DL) model to diagnose early glaucoma from spectral-domain optical coherence tomography (OCT) images.
Adult Humans Deep Learning Gonioscopy Nerve Fibers Sensitivity and Specificity Glaucoma ROC Curve Retinal Ganglion Cells Intraocular Pressure Female Area Under Curve Male Visual Fields Reproducibility of Results Tomography, Optical Coherence Middle Aged Aged Datasets as Topic Visual Field Tests Transfer, Psychology Glaucoma, Open-Angle
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